library_correlations¶
This script calculates stats about functional selections and variants present in libraries.
The median of all LibA selections vs the median of all LibB selections
All selections for a specific condition
Histogram of variants by # of mutations
Distribution of functional scores
written by Brendan Larsen
In [1]:
# this cell is tagged as parameters for `papermill` parameterization
altair_config = None
nipah_config = None
codon_variants_file = None
CHO_corr_plot_save = None
CHO_EFNB2_indiv_plot_save = None
CHO_EFNB3_indiv_plot_save = None
histogram_plot = None
func_scores_plot = None
uniq_barcodes_per_lib_df = None
In [2]:
# Parameters
nipah_config = "nipah_config.yaml"
altair_config = "data/custom_analyses_data/theme.py"
codon_variants_file = "results/variants/codon_variants.csv"
CHO_corr_plot_save = "results/images/CHO_corr_plot_save.html"
CHO_EFNB2_indiv_plot_save = "results/images/CHO_EFNB2_all_corrs.html"
CHO_EFNB3_indiv_plot_save = "results/images/CHO_EFNB3_all_corrs.html"
histogram_plot = "results/images/variants_histogram.html"
func_scores_plot = "results/images/func_scores_distribution.html"
uniq_barcodes_per_lib_df = "results/tables/uniq_barcodes_per_lib_df.csv"
In [3]:
import math
import os
import re
import altair as alt
import numpy as np
import pandas as pd
import scipy.stats
import Bio.SeqIO
import yaml
from Bio import AlignIO
from Bio import PDB
from Bio.Align import PairwiseAligner
from collections import Counter
In [4]:
# allow more rows for Altair
_ = alt.data_transformers.disable_max_rows()
if (
os.getcwd()
== "/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/"
):
pass
print("Already in correct directory")
else:
os.chdir(
"/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/"
)
print("Setup in correct directory")
Setup in correct directory
For running interactively:
In [5]:
if histogram_plot is None:
altair_config = "data/custom_analyses_data/theme.py"
nipah_config = "nipah_config.yaml"
codon_variants_file = "results/variants/codon_variants.csv"
Read in config files
In [6]:
if altair_config:
with open(altair_config, "r") as file:
exec(file.read())
with open(nipah_config) as f:
config = yaml.safe_load(f)
with open("data/func_effects_config.yml") as y:
config_func = yaml.safe_load(y)
In [7]:
cho_efnb2_low_selections = config_func["avg_func_effects"]["CHO_EFNB2_low"][
"selections"
]
LibA_CHO_EFNB2 = [
selection + "_func_effects.csv"
for selection in cho_efnb2_low_selections
if "LibA" in selection and "LibB" not in selection
]
LibB_CHO_EFNB2 = [
selection + "_func_effects.csv"
for selection in cho_efnb2_low_selections
if "LibB" in selection and "LibA" not in selection
]
cho_efnb3_low_selections = config_func["avg_func_effects"]["CHO_EFNB3_low"][
"selections"
]
LibA_CHO_EFNB3 = [
selection + "_func_effects.csv"
for selection in cho_efnb3_low_selections
if "LibA" in selection and "LibB" not in selection
]
LibB_CHO_EFNB3 = [
selection + "_func_effects.csv"
for selection in cho_efnb3_low_selections
if "LibB" in selection and "LibA" not in selection
]
Calculate correlations for LibA and LibB for CHO-EFNB2 cell entry selections¶
In [8]:
# Define the base directory where CSV files are stored
path = "results/func_effects/by_selection/"
# Function to process functional selections from a specific library
def process_func_selections(library, library_name):
df_list = [] # Initialize a list to store dataframes
clock = 1 # Counter to uniquely name columns for each file
# Loop through each file in the library
for file_name in library:
file_path = os.path.join(path, file_name) # Construct the full file path
# Read the CSV file into a dataframe, then rename and drop specific columns
func_scores = pd.read_csv(file_path)
func_scores_renamed = func_scores.rename(
columns={
"functional_effect": f"functional_effect_{clock}",
"times_seen": f"times_seen_{clock}",
}
)
func_scores_renamed.drop(["latent_phenotype_effect"], axis=1, inplace=True)
df_list.append(func_scores_renamed) # Append modified dataframe to the list
clock += 1 # Increment counter
# Merge all dataframes on 'site', 'mutant', and 'wildtype' columns
merged_df = df_list[0]
for df in df_list[1:]:
merged_df = pd.merge(
merged_df, df, on=["site", "mutant", "wildtype"], how="outer"
)
# Calculate median values of functional effects and times seen across all files
lib_columns_func = [col for col in merged_df.columns if "functional_effect" in col]
merged_df[f"mean_effect_{library_name}"] = merged_df[lib_columns_func].mean(
axis=1
)
lib_columns_times_seen = [col for col in merged_df.columns if "times_seen" in col]
merged_df[f"mean_times_seen_{library_name}"] = merged_df[
lib_columns_times_seen
].mean(axis=1)
# Drop intermediate columns used for calculations
lib_columns = [col for col in merged_df.columns if re.search(r"_\d+", col)]
merged_df = merged_df.drop(columns=lib_columns)
return merged_df
# Process selections for two libraries and two cell types
A_selections_E2 = process_func_selections(LibA_CHO_EFNB2, "LibA")
B_selections_E2 = process_func_selections(LibB_CHO_EFNB2, "LibB")
A_selections_E3 = process_func_selections(LibA_CHO_EFNB3, "LibA")
B_selections_E3 = process_func_selections(LibB_CHO_EFNB3, "LibB")
# Function to merge selections from two libraries
def merge_selections(A_selections, B_selections):
merged_selections = pd.merge(
A_selections, B_selections, on=["wildtype", "site", "mutant"], how="inner"
)
lib_columns_times_seen = [
col for col in merged_selections.columns if "times_seen" in col
]
merged_selections["times_seen"] = merged_selections[lib_columns_times_seen].median(
axis=1
)
return merged_selections
# Merge selections and add cell type information
CHO_EFNB2_merged = merge_selections(A_selections_E2, B_selections_E2)
CHO_EFNB2_merged["cell_type"] = "CHO-EFNB2"
CHO_EFNB3_merged = merge_selections(A_selections_E3, B_selections_E3)
CHO_EFNB3_merged["cell_type"] = "CHO-EFNB3"
# Concatenate merged selections for both cell types
both_selections = pd.concat([CHO_EFNB2_merged, CHO_EFNB3_merged])
# Function to generate and display a scatter plot comparing median effects from two libraries
def make_chart_median(df, title):
slider = alt.binding_range(min=1, max=25, step=1, name="times_seen")
selector = alt.param(name="SelectorName", value=1, bind=slider)
empty = []
variant_selector = alt.selection_point(
on="mouseover", empty=False, nearest=True, fields=["site", "mutant"], value=1
)
df = df[(df["mean_effect_LibA"].notna()) & (df["mean_effect_LibB"].notna())]
size = df.shape[0]
for selection in ["CHO-EFNB2", "CHO-EFNB3"]:
print(selection)
tmp_df = df[df["cell_type"] == selection]
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
df[f"mean_effect_LibA"], df[f"mean_effect_LibB"]
)
r_value = float(r_value)
print(f"{r_value:.2f}")
chart = (
alt.Chart(tmp_df, title=f"Entry in {selection} cells")
.mark_point()
.encode(
x=alt.X("mean_effect_LibA", title="LibA Cell Entry"),
y=alt.Y("mean_effect_LibB", title="LibB Cell Entry"),
tooltip=["site", "wildtype", "mutant", "times_seen"],
size=alt.condition(variant_selector, alt.value(100), alt.value(15)),
color=alt.condition(
alt.datum.times_seen < selector,
alt.value("transparent"),
alt.value("black"),
),
opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.2)),
)
)
empty.append(chart)
combined_effect_chart = (
alt.hconcat(*empty)
.resolve_scale(y="shared", x="shared", color="independent")
.add_params(variant_selector, selector)
)
return combined_effect_chart
CHO_EFNB2_corr_plot = make_chart_median(both_selections, "CHO-EFNB2")
CHO_EFNB2_corr_plot.display()
if histogram_plot is not None:
CHO_EFNB2_corr_plot.save(CHO_corr_plot_save)
CHO-EFNB2 0.92 CHO-EFNB3 0.92
In [9]:
def plot_corr_heatmap(df):
empty_chart = []
for cell in list(df["cell_type"].unique()):
tmp_df = df[df["cell_type"] == cell]
chart = (
alt.Chart(tmp_df, title=f"{cell}")
.mark_rect()
.encode(
x=alt.X("mean_effect_LibA", title="Library A",axis=alt.Axis(values=[-4,-3,-2,-1,0,1])).bin(
maxbins=50,
),
y=alt.Y("mean_effect_LibB", title="Library B",axis=alt.Axis(values=[-4,-3,-2,-1,0,1])).bin(
maxbins=50,
),
color=alt.Color("count()", title="Count").scale(scheme="greenblue",type='log'),
# tooltip=['effect','binding_median']
)
)
empty_chart.append(chart)
combined_chart = alt.hconcat(
*empty_chart, title=alt.Title("Correlation between cell entry measurements between libraries",offset=10)
).resolve_scale(y="shared", x="shared", color="shared")
return combined_chart
entry_binding_corr_heatmap = plot_corr_heatmap(both_selections)
entry_binding_corr_heatmap.display()
# entry_binding_corr_heatmap.save(entry_binding_corr_heatmap)
Make correlations between individual selections¶
In [10]:
def process_individ_selections(library):
df_list = []
clock = 1
for file_name in library:
file_path = os.path.join(path, file_name)
print(f"Processing file: {file_path}")
# Read the current CSV file
func_scores = pd.read_csv(file_path)
func_scores_renamed = func_scores.rename(
columns={
"functional_effect": f"functional_effect_{clock}",
"times_seen": f"times_seen_{clock}",
}
)
func_scores_renamed.drop(["latent_phenotype_effect"], axis=1, inplace=True)
# Append the dataframe to the list
df_list.append(func_scores_renamed)
clock = clock + 1
# Merge all dataframes on 'site' and 'mutant'
merged_df = df_list[0]
for df in df_list[1:]:
merged_df = pd.merge(
merged_df, df, on=["site", "mutant", "wildtype"], how="outer"
)
# Make list of how many selections are done for later correlation plots
lib_size = len(library)
number_list = [i for i in range(1, lib_size + 1)]
return merged_df, number_list
CHO_EFNB2_indiv, lib_size_EFNB2 = process_individ_selections(
LibA_CHO_EFNB2 + LibB_CHO_EFNB2
)
CHO_EFNB3_indiv, lib_size_EFNB3 = process_individ_selections(
LibA_CHO_EFNB3 + LibB_CHO_EFNB3
)
def make_chart(df, number_list):
empty_list = []
for i in number_list:
other_empty_list = []
for j in number_list:
df = df[
(df[f"times_seen_{i}"] >= config["func_times_seen_cutoff"])
& (df[f"times_seen_{j}"] >= config["func_times_seen_cutoff"])
& (df[f"functional_effect_{i}"].notna())
& (df[f"functional_effect_{j}"].notna())
]
chart = (
alt.Chart(df)
.mark_circle(size=10, color="black", opacity=0.2)
.encode(
x=alt.X(f"functional_effect_{i}"),
y=alt.Y(f"functional_effect_{j}"),
tooltip=["site", "wildtype", "mutant"],
)
.properties(height=alt.Step(10), width=alt.Step(10))
)
other_empty_list.append(chart)
combined_effect_chart = alt.hconcat(*other_empty_list).resolve_scale(
y="shared", x="shared", color="independent"
)
empty_list.append(combined_effect_chart)
final_combined_chart = alt.vconcat(*empty_list).resolve_scale(
y="shared", x="shared", color="independent"
)
return final_combined_chart
CHO_EFNB2_indiv_plot = make_chart(CHO_EFNB2_indiv, lib_size_EFNB2)
if histogram_plot is not None:
CHO_EFNB2_indiv_plot.save(CHO_EFNB2_indiv_plot_save)
CHO_EFNB3_indiv_plot = make_chart(CHO_EFNB3_indiv, lib_size_EFNB3)
if histogram_plot is not None:
CHO_EFNB3_indiv_plot.save(CHO_EFNB3_indiv_plot_save)
Processing file: results/func_effects/by_selection/LibA-231112-CHO-EFNB2-BA6-nac_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231207-CHO-EFNB2-BA6-1_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231207-CHO-EFNB2-BA6-2_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231207-CHO-EFNB2-BA6-3_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231207-CHO-EFNB2-BA6-pool_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231222-CHO-EFNB2-BA6-nac_diffVSV_func_effects.csv Processing file: results/func_effects/by_selection/LibB-231112-CHO-EFNB2-BA6-nac_diff_VSV_func_effects.csv Processing file: results/func_effects/by_selection/LibB-231116-CHO-BA6_PREV_POOL_func_effects.csv Processing file: results/func_effects/by_selection/LibA-230725-CHO-EFNB3-C6-nac-diffVSV_func_effects.csv Processing file: results/func_effects/by_selection/LibA-230916-CHO-EFNB2-BA6-nac_diffVSV_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231024-CHO-EFNB3-C6-nac_func_effects.csv Processing file: results/func_effects/by_selection/LibB-230720-CHO-C6-nac-VSV_func_effects.csv Processing file: results/func_effects/by_selection/LibB-230815-CHO-C6-nac_func_effects.csv Processing file: results/func_effects/by_selection/LibB-230818-CHO-C6-nac_func_effects.csv Processing file: results/func_effects/by_selection/LibB-231116-CHO-C6_PREV_POOL_func_effects.csv
Now make histogram of variants¶
In [11]:
codon_variants = pd.read_csv(codon_variants_file)
display(codon_variants.head(3))
unique_barcodes_per_library = codon_variants.groupby("library")["barcode"].nunique()
uniq_barcodes_per_lib = pd.DataFrame(unique_barcodes_per_library)
display(uniq_barcodes_per_lib)
| target | library | barcode | variant_call_support | codon_substitutions | aa_substitutions | n_codon_substitutions | n_aa_substitutions | |
|---|---|---|---|---|---|---|---|---|
| 0 | gene | LibA | AAAAAAAAAAAAAGAA | 5 | ACC461ACT ATC475AGC | I475S | 2 | 1 |
| 1 | gene | LibA | AAAAAAAAAAACCCAT | 36 | GCG16GAG CAG23GAG | A16E Q23E | 2 | 2 |
| 2 | gene | LibA | AAAAAAAAAAAGTTTC | 6 | TAC319CCC | Y319P | 1 | 1 |
| barcode | |
|---|---|
| library | |
| LibA | 78450 |
| LibB | 60623 |
Find which sites are present, and which are missing¶
In [12]:
# Initialize an empty dictionary to keep track of observed mutations
aa_counts = {}
wildtypes = {} # Dictionary to keep track of wildtype amino acids for each site
# Function to process each cell, update counts, and record wildtype amino acids
def process_cell(cell):
if pd.notna(cell): # Check if cell is not NaN
substitutions = cell.split()
for substitution in substitutions:
if substitution[-1] not in ("*", "-") and substitution[0] not in (
"*"
): # Skip if substitution ends with '*' or '-'
site = substitution[1:-1]
mutation = substitution[-1]
wildtype = substitution[0]
site_mutation = site + mutation
if site not in wildtypes:
wildtypes[site] = wildtype
if site_mutation in aa_counts:
aa_counts[site_mutation] += 1
else:
aa_counts[site_mutation] = 1
empty_mutants = []
empty_percent = []
for lib in ["LibA", "LibB"]:
# Apply the function to each cell in the 'aa_substitutions' column
tmp_df = codon_variants[codon_variants["library"] == lib]
tmp_df["aa_substitutions"].apply(process_cell)
# Generate all possible combinations excluding the wildtype for each site
expected_sites = range(1, 533)
possible_mutations = [
"A",
"C",
"D",
"E",
"F",
"G",
"H",
"I",
"K",
"L",
"M",
"N",
"P",
"Q",
"R",
"S",
"T",
"V",
"W",
"Y",
]
# Adjust expected combinations to exclude the wildtype for each site
expected_combinations = set()
for site in expected_sites:
site_str = str(site)
if site_str in wildtypes:
wildtype = wildtypes[site_str]
for mutation in possible_mutations:
if mutation != wildtype: # Exclude the wildtype amino acid
expected_combinations.add(site_str + mutation)
# Extract the actual combinations from the counts
actual_combinations = set(aa_counts.keys())
# Find missing combinations
missing_combinations = expected_combinations - actual_combinations
# Display results
print(f"Number of unique site-mutation combinations observed: {len(aa_counts)}")
print(
f"Number of missing combinations (excluding wildtypes): {len(missing_combinations)}"
)
print(
f"Total possible combinations excluding wildtypes: {len(expected_combinations)}"
)
empty_percent.append(len(actual_combinations) / len(expected_combinations))
uniq_barcodes_per_lib["percent"] = empty_percent
uniq_barcodes_per_lib["percent"] = uniq_barcodes_per_lib["percent"] * 100
uniq_barcodes_per_lib = uniq_barcodes_per_lib.round(2)
uniq_barcodes_per_lib = uniq_barcodes_per_lib.reset_index()
uniq_barcodes_per_lib.to_csv(uniq_barcodes_per_lib_df, index=False)
Number of unique site-mutation combinations observed: 10055 Number of missing combinations (excluding wildtypes): 54 Total possible combinations excluding wildtypes: 10108 Number of unique site-mutation combinations observed: 10080 Number of missing combinations (excluding wildtypes): 29 Total possible combinations excluding wildtypes: 10108
In [13]:
def calculate_fraction(library):
total_A = codon_variants[codon_variants["library"] == library].shape[0]
for number in range(5):
fraction = codon_variants[
(codon_variants["library"] == library)
& (codon_variants["n_aa_substitutions"] == number)
].shape[0]
print(
f"For {library}, the fraction of sites with {number} mutations relative to WT are: {fraction/total_A:.2f}"
)
calculate_fraction("LibA")
calculate_fraction("LibB")
For LibA, the fraction of sites with 0 mutations relative to WT are: 0.11 For LibA, the fraction of sites with 1 mutations relative to WT are: 0.64 For LibA, the fraction of sites with 2 mutations relative to WT are: 0.22 For LibA, the fraction of sites with 3 mutations relative to WT are: 0.03 For LibA, the fraction of sites with 4 mutations relative to WT are: 0.00 For LibB, the fraction of sites with 0 mutations relative to WT are: 0.11 For LibB, the fraction of sites with 1 mutations relative to WT are: 0.65 For LibB, the fraction of sites with 2 mutations relative to WT are: 0.21 For LibB, the fraction of sites with 3 mutations relative to WT are: 0.03 For LibB, the fraction of sites with 4 mutations relative to WT are: 0.00
In [14]:
def plot_histogram(df):
df = df.drop(
columns=[
"barcode",
"target",
"variant_call_support",
"codon_substitutions",
"aa_substitutions",
"n_codon_substitutions",
]
)
chart = (
alt.Chart(df)
.mark_bar(color="black")
.encode(
alt.X("n_aa_substitutions:N", title="# of AA Substitutions"),
alt.Y(
"count()", title="Count", axis=alt.Axis(grid=True)
), # count() is a built-in aggregation to count rows in each bin
column=alt.Column(
"library", header=alt.Header(title=None, labelFontSize=18)
),
)
)
return chart
histogram = plot_histogram(codon_variants)
histogram.display()
if histogram_plot is not None:
histogram.save(histogram_plot)
Find distribution of functional scores¶
In [15]:
def pull_in_func_scores(df):
empty_list = []
for i in df:
j = i + "_func_scores.csv"
tmp_df = pd.read_csv(f"results/func_scores/{j}")
tmp_df["selection"] = i
empty_list.append(tmp_df)
tmp_df = pd.concat(empty_list)
return tmp_df
e2_func_scores_df = pull_in_func_scores(cho_efnb2_low_selections)
e2_func_scores_df["cell_type"] = "CHO-EFNB2"
e3_func_scores_df = pull_in_func_scores(cho_efnb3_low_selections)
e3_func_scores_df["cell_type"] = "CHO-EFNB3"
# Make combined dataframe of cell entry data
merged_func_scores = pd.concat([e2_func_scores_df, e3_func_scores_df])
def classify_mutation(row):
if isinstance(row["aa_substitutions"], str) and "*" in row["aa_substitutions"]:
return "stop"
elif row["n_aa_substitutions"] == 0:
if row["n_codon_substitutions"] >= 1:
return "synonymous"
else:
return "wildtype"
elif row["n_aa_substitutions"] == 1:
return "1 nonsynonymous"
elif row["n_aa_substitutions"] >= 2:
return ">2 nonsynonymous"
# Apply the function to each row in the dataframe to create the new column
merged_func_scores["mutation_class"] = merged_func_scores.apply(
classify_mutation, axis=1
)
result_df = (
merged_func_scores.groupby(["barcode", "cell_type"])
.agg(
func_score=("func_score", "median"), mutation_class=("mutation_class", "first")
)
.reset_index()
)
tmp = (
result_df.groupby(["mutation_class", "cell_type"])["func_score"]
.median()
.reset_index()
)
tmp = tmp.rename(columns={"func_score": "median_func_score"})
result_df = result_df.merge(tmp, on=["mutation_class", "cell_type"], how="left")
display(result_df)
| barcode | cell_type | func_score | mutation_class | median_func_score | |
|---|---|---|---|---|---|
| 0 | AAAAAAAAAAGACCCG | CHO-EFNB2 | -2.20700 | >2 nonsynonymous | -1.8130 |
| 1 | AAAAAAAAAAGACCCG | CHO-EFNB3 | 0.03215 | >2 nonsynonymous | -3.0560 |
| 2 | AAAAAAAAACCTATAG | CHO-EFNB2 | -0.17420 | 1 nonsynonymous | -0.6460 |
| 3 | AAAAAAAAACCTATAG | CHO-EFNB3 | -0.85940 | 1 nonsynonymous | -0.9497 |
| 4 | AAAAAAAAATCCTACG | CHO-EFNB2 | -0.86490 | 1 nonsynonymous | -0.6460 |
| ... | ... | ... | ... | ... | ... |
| 128340 | TTTTTTCGATGAACGA | CHO-EFNB3 | -4.15400 | >2 nonsynonymous | -3.0560 |
| 128341 | TTTTTTGCCAAGTGAA | CHO-EFNB2 | -0.50200 | >2 nonsynonymous | -1.8130 |
| 128342 | TTTTTTTAAGACTACA | CHO-EFNB3 | -2.23100 | 1 nonsynonymous | -0.9497 |
| 128343 | TTTTTTTACTCGAATG | CHO-EFNB2 | -0.46220 | 1 nonsynonymous | -0.6460 |
| 128344 | TTTTTTTACTCGAATG | CHO-EFNB3 | 1.59500 | 1 nonsynonymous | -0.9497 |
128345 rows × 5 columns
In [16]:
def plot_func_score_distribution(df):
custom_sort = [
"wildtype",
"synonymous",
"1 nonsynonymous",
">2 nonsynonymous",
"stop",
]
empty_charts = []
for cell_idx, target_cell in enumerate(["CHO-EFNB2", "CHO-EFNB3"]):
charts = []
first_df = df[df["cell_type"] == target_cell]
for idx, subset in enumerate(custom_sort):
tmp_df = first_df[first_df["mutation_class"] == subset]
is_last_plot = idx == len(custom_sort) - 1
x_axis = alt.Axis(
labelAngle=-90,
titleFontSize=10,
tickCount=3,
values=[-10, -5, 0],
title="Functional Score" if is_last_plot else None,
labels=True if is_last_plot else False,
) # Only show labels for the last plot
first_plot_column = cell_idx == 0
y_axis = alt.Axis(
labelAngle=0,
titleAngle=0,
title=subset if first_plot_column else None,
domain=False,
ticks=False,
labels=False,
titleX=-10,
titleAlign="right",
)
chart = (
alt.Chart(tmp_df, title=(target_cell if idx == 0 else ""))
.mark_area(color="black")
.encode(
x=alt.X("func_score", bin=alt.Bin(step=0.4), axis=x_axis),
y=alt.Y(
"count()", title=subset, axis=y_axis
), # alt.Axis(domain=False, ticks=False, labels=False)),
color=alt.Color(
"median_func_score",
title="Median Functional Score",
scale=alt.Scale(scheme="greenblue"),
),
# row=alt.Row('mutation_class', title=None, sort=custom_sort, header=alt.Header(title=None)),
# column=alt.Column('cell_type'),
)
.properties(width=100, height=50)
)
charts.append(chart)
combined_muts_chart = alt.vconcat(*charts, spacing=0).resolve_scale(
y="independent", x="shared", color="shared"
)
empty_charts.append(combined_muts_chart)
# Combine charts using vertical concatenation, adjusting scales and configuration as needed
combined_chart = (
alt.hconcat(*empty_charts, spacing=0)
.resolve_scale(y="independent", x="shared", color="shared")
.configure_view(stroke=None)
.configure_axis(grid=False)
.configure_title(
anchor="middle", # Anchors the title to the start of the chart
offset=5, # Adjusts the distance between the title and the chart
fontSize=16, # Adjusts the font size of the title
# dx=5, # Shifts the title horizontally (use negative value to shift left)
# dy=-5 # Shifts the title vertically (use negative value to shift up)
)
)
return combined_chart
tmp_img = plot_func_score_distribution(result_df)
tmp_img.display()
if histogram_plot is not None:
tmp_img.save(func_scores_plot)